Overview

Dataset statistics

Number of variables31
Number of observations6095
Missing cells6576
Missing cells (%)3.5%
Duplicate rows74
Duplicate rows (%)1.2%
Total size in memory1.4 MiB
Average record size in memory248.0 B

Variable types

CAT23
NUM8

Warnings

Dataset has 74 (1.2%) duplicate rows Duplicates
Carrera has a high cardinality: 495 distinct values High cardinality
Universidad has a high cardinality: 799 distinct values High cardinality
rol_trabajo has a high cardinality: 334 distinct values High cardinality
tecnologies has a high cardinality: 5452 distinct values High cardinality
Carrera has 299 (4.9%) missing values Missing
Universidad has 534 (8.8%) missing values Missing
sueldo_dolarizado has 5419 (88.9%) missing values Missing
violencia_laboral has 141 (2.3%) missing values Missing
Orientación sexual has 183 (3.0%) missing values Missing
edad is highly skewed (γ1 = 78.04429782) Skewed
personas_a_cargo is highly skewed (γ1 = 71.5168196) Skewed
experiencia_anios has 318 (5.2%) zeros Zeros
empresa_actual_anios has 1538 (25.2%) zeros Zeros
personas_a_cargo has 4592 (75.3%) zeros Zeros
sueldo_ajuste_total_2020 has 2685 (44.1%) zeros Zeros

Reproduction

Analysis started2020-11-30 05:57:26.825507
Analysis finished2020-11-30 05:57:50.930549
Duration24.11 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

genero
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Hombre
5122 
Mujer
942 
Otros
 
31
ValueCountFrequency (%) 
Hombre512284.0%
 
Mujer94215.5%
 
Otros310.5%
 
2020-11-30T02:57:51.066670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:51.276792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:51.581398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.840360952
Min length5

edad
Real number (ℝ≥0)

SKEWED

Distinct50
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.18195242
Minimum18
Maximum52000
Zeros0
Zeros (%)0.0%
Memory size47.6 KiB
2020-11-30T02:57:51.889398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile23
Q127
median31
Q337
95-th percentile46
Maximum52000
Range51982
Interquartile range (IQR)10

Descriptive statistics

Standard deviation665.7211745
Coefficient of variation (CV)16.16536214
Kurtosis6092.265324
Mean41.18195242
Median Absolute Deviation (MAD)5
Skewness78.04429782
Sum251004
Variance443184.6822
MonotocityNot monotonic
2020-11-30T02:57:52.220187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
293806.2%
 
283646.0%
 
303565.8%
 
313415.6%
 
263225.3%
 
333085.1%
 
273055.0%
 
253004.9%
 
322914.8%
 
342884.7%
 
Other values (40)284046.6%
 
ValueCountFrequency (%) 
182< 0.1%
 
19230.4%
 
20490.8%
 
21771.3%
 
221332.2%
 
ValueCountFrequency (%) 
520001< 0.1%
 
5671< 0.1%
 
671< 0.1%
 
651< 0.1%
 
643< 0.1%
 

ubicacion
Categorical

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Ciudad Autónoma de Buenos Aires
3764 
GBA
524 
Provincia de Buenos Aires
497 
Córdoba
456 
Santa Fe
 
354
Other values (20)
500 
ValueCountFrequency (%) 
Ciudad Autónoma de Buenos Aires376461.8%
 
GBA5248.6%
 
Provincia de Buenos Aires4978.2%
 
Córdoba4567.5%
 
Santa Fe3545.8%
 
Mendoza1071.8%
 
Entre Ríos661.1%
 
Río Negro380.6%
 
Neuquén360.6%
 
Jujuy360.6%
 
Other values (15)2173.6%
 
2020-11-30T02:57:52.612329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:52.860194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length31
Mean length23.08088597
Min length3

experiencia_anios
Real number (ℝ≥0)

ZEROS

Distinct59
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.930861362
Minimum0
Maximum115
Zeros318
Zeros (%)5.2%
Memory size47.6 KiB
2020-11-30T02:57:53.085329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q313
95-th percentile22
Maximum115
Range115
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.161094025
Coefficient of variation (CV)0.8018368817
Kurtosis8.451486768
Mean8.930861362
Median Absolute Deviation (MAD)5
Skewness1.475538822
Sum54433.6
Variance51.28126764
MonotocityNot monotonic
2020-11-30T02:57:53.323519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105509.0%
 
54837.9%
 
34637.6%
 
24577.5%
 
154036.6%
 
43806.2%
 
13525.8%
 
03185.2%
 
62904.8%
 
202824.6%
 
Other values (49)211734.7%
 
ValueCountFrequency (%) 
03185.2%
 
13525.8%
 
1.5260.4%
 
1.61< 0.1%
 
1.72< 0.1%
 
ValueCountFrequency (%) 
1151< 0.1%
 
441< 0.1%
 
431< 0.1%
 
401< 0.1%
 
391< 0.1%
 

empresa_actual_anios
Real number (ℝ≥0)

ZEROS

Distinct45
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.311944217
Minimum0
Maximum43
Zeros1538
Zeros (%)25.2%
Memory size47.6 KiB
2020-11-30T02:57:53.557082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile13
Maximum43
Range43
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.367741538
Coefficient of variation (CV)1.318784754
Kurtosis7.630173195
Mean3.311944217
Median Absolute Deviation (MAD)2
Skewness2.381417439
Sum20186.3
Variance19.07716614
MonotocityNot monotonic
2020-11-30T02:57:53.793895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
0153825.2%
 
1117619.3%
 
293015.3%
 
35529.1%
 
53355.5%
 
43085.1%
 
61843.0%
 
101582.6%
 
71402.3%
 
81332.2%
 
Other values (35)64110.5%
 
ValueCountFrequency (%) 
0153825.2%
 
0.560.1%
 
1117619.3%
 
1.31< 0.1%
 
1.450.1%
 
ValueCountFrequency (%) 
431< 0.1%
 
401< 0.1%
 
341< 0.1%
 
301< 0.1%
 
293< 0.1%
 

personas_a_cargo
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.211648893
Minimum0
Maximum2500
Zeros4592
Zeros (%)75.3%
Memory size47.6 KiB
2020-11-30T02:57:54.023594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum2500
Range2500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32.98339473
Coefficient of variation (CV)14.91348597
Kurtosis5400.61155
Mean2.211648893
Median Absolute Deviation (MAD)0
Skewness71.5168196
Sum13480
Variance1087.904328
MonotocityNot monotonic
2020-11-30T02:57:54.245159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0459275.3%
 
22774.5%
 
12504.1%
 
31782.9%
 
41752.9%
 
51612.6%
 
6651.1%
 
8591.0%
 
10510.8%
 
7410.7%
 
Other values (41)2464.0%
 
ValueCountFrequency (%) 
0459275.3%
 
12504.1%
 
22774.5%
 
31782.9%
 
41752.9%
 
ValueCountFrequency (%) 
25001< 0.1%
 
3001< 0.1%
 
1701< 0.1%
 
1501< 0.1%
 
1381< 0.1%
 
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Universitario
4133 
Terciario
1135 
Secundario
449 
Posgrado
 
342
Doctorado
 
30
Other values (2)
 
6
ValueCountFrequency (%) 
Universitario413367.8%
 
Terciario113518.6%
 
Secundario4497.4%
 
Posgrado3425.6%
 
Doctorado300.5%
 
Posdoctorado40.1%
 
Primario2< 0.1%
 
2020-11-30T02:57:54.448681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:54.569439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:54.748559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length13
Mean length11.73158326
Min length8
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Completado
2907 
En curso
1684 
Incompleto
1504 
ValueCountFrequency (%) 
Completado290747.7%
 
En curso168427.6%
 
Incompleto150424.7%
 
2020-11-30T02:57:54.951941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:55.091761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:55.218150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length9.447415915
Min length8

Carrera
Categorical

HIGH CARDINALITY
MISSING

Distinct495
Distinct (%)8.5%
Missing299
Missing (%)4.9%
Memory size47.6 KiB
Ingeniería en Sistemas de Información
1194 
Ingeniería en Informática
784 
Analista de Sistemas
596 
Licenciatura en Ciencias dela Computación
300 
Licenciatura en Sistemas de Información
298 
Other values (490)
2624 
ValueCountFrequency (%) 
Ingeniería en Sistemas de Información119419.6%
 
Ingeniería en Informática78412.9%
 
Analista de Sistemas5969.8%
 
Licenciatura en Ciencias dela Computación3004.9%
 
Licenciatura en Sistemas de Información2984.9%
 
Licenciatura en Informática2564.2%
 
Tecnicatura en Programación1903.1%
 
Ingeniería Electrónica1552.5%
 
Diseño Gráfico1392.3%
 
Tecnicatura Superior en Programación1342.2%
 
Other values (485)175028.7%
 
(Missing)2994.9%
 
2020-11-30T02:57:55.434668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique395 ?
Unique (%)6.8%
2020-11-30T02:57:55.696568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length120
Median length27
Mean length27.81066448
Min length1

Universidad
Categorical

HIGH CARDINALITY
MISSING

Distinct799
Distinct (%)14.4%
Missing534
Missing (%)8.8%
Memory size47.6 KiB
UTN - Universidad Tecnológica Nacional
1378 
UBA - Universidad de Buenos Aires
785 
UNLaM - Universidad Nacional de La Matanza
 
236
UADE - Universidad Argentina De la Empresa
 
204
UAI - Universidad Abierta Interamericana
 
193
Other values (794)
2765 
ValueCountFrequency (%) 
UTN - Universidad Tecnológica Nacional137822.6%
 
UBA - Universidad de Buenos Aires78512.9%
 
UNLaM - Universidad Nacional de La Matanza2363.9%
 
UADE - Universidad Argentina De la Empresa2043.3%
 
UAI - Universidad Abierta Interamericana1933.2%
 
UNLP - Universidad Nacional de La Plata1843.0%
 
ORT1131.9%
 
UP - Universidad de Palermo1101.8%
 
Universidad Siglo 211031.7%
 
UNC - Universidad Nacional de Córdoba941.5%
 
Other values (789)216135.5%
 
(Missing)5348.8%
 
2020-11-30T02:57:55.975934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique614 ?
Unique (%)11.0%
2020-11-30T02:57:56.485470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length155
Median length37
Mean length30.60032814
Min length1
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Sí, de forma particular
2650 
No
1417 
Sí, de forma particular, Sí, los pagó un empleador
1339 
Sí, los pagó un empleador
668 
No, Sí, de forma particular
 
18
Other values (2)
 
3
ValueCountFrequency (%) 
Sí, de forma particular265043.5%
 
No141723.2%
 
Sí, de forma particular, Sí, los pagó un empleador133922.0%
 
Sí, los pagó un empleador66811.0%
 
No, Sí, de forma particular180.3%
 
No, Sí, los pagó un empleador2< 0.1%
 
No, Sí, de forma particular, Sí, los pagó un empleador1< 0.1%
 
2020-11-30T02:57:56.706234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-11-30T02:57:56.833610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:57.030061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length54
Median length23
Mean length24.28744873
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
No
5211 
884 
ValueCountFrequency (%) 
No521185.5%
 
88414.5%
 
2020-11-30T02:57:57.206768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:57.324418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:57.460311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
3268 
No
2827 
ValueCountFrequency (%) 
326853.6%
 
No282746.4%
 
2020-11-30T02:57:57.634570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:57.752735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:57.869588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

rol_trabajo
Categorical

HIGH CARDINALITY

Distinct334
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Developer
2402 
SysAdmin / DevOps / SRE
689 
Technical Leader
399 
QA / Tester
254 
Manager / Director
 
231
Other values (329)
2120 
ValueCountFrequency (%) 
Developer240239.4%
 
SysAdmin / DevOps / SRE68911.3%
 
Technical Leader3996.5%
 
QA / Tester2544.2%
 
Manager / Director2313.8%
 
HelpDesk1983.2%
 
Project Manager1933.2%
 
Architect1752.9%
 
Data Scientist / Data Engineer1402.3%
 
Consultant1392.3%
 
Other values (324)127520.9%
 
2020-11-30T02:57:58.094119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique287 ?
Unique (%)4.7%
2020-11-30T02:57:58.352952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length124
Median length9
Mean length13.17456932
Min length1
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Windows
3442 
GNU/Linux
1356 
macOS
1292 
*BSD
 
5
ValueCountFrequency (%) 
Windows344256.5%
 
GNU/Linux135622.2%
 
macOS129221.2%
 
*BSD50.1%
 
2020-11-30T02:57:58.790029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:59.041960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:59.200128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length7.018539787
Min length4

celular_so
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Android
4868 
iOS
1157 
No tengo celular / no es Smartphone
 
60
Windows
 
10
ValueCountFrequency (%) 
Android486879.9%
 
iOS115719.0%
 
No tengo celular / no es Smartphone601.0%
 
Windows100.2%
 
2020-11-30T02:57:59.386736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:59.517148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:59.670631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length35
Median length7
Mean length6.516324856
Min length3

guardias
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
No
4640 
Sí, pasiva
1183 
Sí, activa
 
272
ValueCountFrequency (%) 
No464076.1%
 
Sí, pasiva118319.4%
 
Sí, activa2724.5%
 
2020-11-30T02:57:59.848282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:57:59.967430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:00.109918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length2
Mean length3.9097621
Min length2

Tipo de contrato
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Full-Time
5166 
Part-Time
 
310
Remoto (empresa de otro país)
 
231
Tercerizado (trabajo a través de consultora o agencia)
 
219
Freelance
 
169
ValueCountFrequency (%) 
Full-Time516684.8%
 
Part-Time3105.1%
 
Remoto (empresa de otro país)2313.8%
 
Tercerizado (trabajo a través de consultora o agencia)2193.6%
 
Freelance1692.8%
 
2020-11-30T02:58:00.313643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:00.443022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:00.598775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length54
Median length9
Mean length11.37489746
Min length9

sueldo_mensual_bruto_ars
Real number (ℝ≥0)

Distinct1459
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124058.5496
Minimum1
Maximum2180000
Zeros0
Zeros (%)0.0%
Memory size47.6 KiB
2020-11-30T02:58:00.795796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26967
Q161595
median96000
Q3145000
95-th percentile307150
Maximum2180000
Range2179999
Interquartile range (IQR)83405

Descriptive statistics

Standard deviation122912.3518
Coefficient of variation (CV)0.990760832
Kurtosis54.61861599
Mean124058.5496
Median Absolute Deviation (MAD)40000
Skewness5.467344355
Sum756136859.9
Variance1.510744623e+10
MonotocityNot monotonic
2020-11-30T02:58:01.021863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1000001762.9%
 
1200001532.5%
 
900001312.1%
 
600001192.0%
 
1400001151.9%
 
1500001151.9%
 
1100001071.8%
 
1300001051.7%
 
800001051.7%
 
70000961.6%
 
Other values (1449)487380.0%
 
ValueCountFrequency (%) 
190.1%
 
22< 0.1%
 
153< 0.1%
 
17.31< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
21800001< 0.1%
 
20800001< 0.1%
 
20000001< 0.1%
 
16800001< 0.1%
 
14391001< 0.1%
 

sueldo_dolarizado
Categorical

MISSING

Distinct1
Distinct (%)0.1%
Missing5419
Missing (%)88.9%
Memory size47.6 KiB
Mi sueldo está dolarizado
676 
ValueCountFrequency (%) 
Mi sueldo está dolarizado67611.1%
 
(Missing)541988.9%
 
2020-11-30T02:58:01.219819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:01.347029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:01.459954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length3
Mean length5.440032814
Min length3
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
3
2670 
2
1897 
4
878 
1
650 
ValueCountFrequency (%) 
3267043.8%
 
2189731.1%
 
487814.4%
 
165010.7%
 
2020-11-30T02:58:01.647045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:01.793868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:01.919599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

sueldo_bonos
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
No
3782 
De uno a tres sueldos
795 
Menos de un sueldo
700 
Un sueldo
656 
3+ sueldos
 
162
ValueCountFrequency (%) 
No378262.1%
 
De uno a tres sueldos79513.0%
 
Menos de un sueldo70011.5%
 
Un sueldo65610.8%
 
3+ sueldos1622.7%
 
2020-11-30T02:58:02.114079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:02.250565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:02.406373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length2
Mean length7.281870386
Min length2
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
No
2671 
Uno
2424 
Dos
787 
Tres
 
117
Más de tres
 
96
ValueCountFrequency (%) 
No267143.8%
 
Uno242439.8%
 
Dos78712.9%
 
Tres1171.9%
 
Más de tres961.6%
 
2020-11-30T02:58:02.628441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:02.763426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:02.908898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length2.706972929
Min length2

sueldo_ajuste_total_2020
Real number (ℝ≥0)

ZEROS

Distinct127
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.4445114
Minimum0
Maximum100
Zeros2685
Zeros (%)44.1%
Memory size47.6 KiB
2020-11-30T02:58:03.105655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q318
95-th percentile35
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.86043294
Coefficient of variation (CV)1.231310154
Kurtosis5.006645803
Mean10.4445114
Median Absolute Deviation (MAD)7
Skewness1.712631342
Sum63659.297
Variance165.3907355
MonotocityNot monotonic
2020-11-30T02:58:03.340152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0268544.1%
 
105218.5%
 
204627.6%
 
154036.6%
 
302273.7%
 
251652.7%
 
51632.7%
 
121422.3%
 
8861.4%
 
7771.3%
 
Other values (117)116419.1%
 
ValueCountFrequency (%) 
0268544.1%
 
0.051< 0.1%
 
0.061< 0.1%
 
0.121< 0.1%
 
0.381< 0.1%
 
ValueCountFrequency (%) 
10070.1%
 
951< 0.1%
 
901< 0.1%
 
891< 0.1%
 
841< 0.1%
 

violencia_laboral
Categorical

MISSING

Distinct3
Distinct (%)0.1%
Missing141
Missing (%)2.3%
Memory size47.6 KiB
Jamás
4096 
En un trabajo anterior
1308 
En mi trabajo actual
550 
ValueCountFrequency (%) 
Jamás409667.2%
 
En un trabajo anterior130821.5%
 
En mi trabajo actual5509.0%
 
(Missing)1412.3%
 
2020-11-30T02:58:03.578314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:03.742501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:03.905612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length5
Mean length9.955537326
Min length3

Orientación sexual
Categorical

MISSING

Distinct42
Distinct (%)0.7%
Missing183
Missing (%)3.0%
Memory size47.6 KiB
Heterosexual
4905 
¿Qué les importa?
535 
Bisexual o queer
 
255
Homosexual
 
173
Pansexual
 
5
Other values (37)
 
39
ValueCountFrequency (%) 
Heterosexual490580.5%
 
¿Qué les importa?5358.8%
 
Bisexual o queer2554.2%
 
Homosexual1732.8%
 
Pansexual50.1%
 
Pansexual 2< 0.1%
 
Demisexual2< 0.1%
 
Juajua1< 0.1%
 
others1< 0.1%
 
¿?1< 0.1%
 
Other values (32)320.5%
 
(Missing)1833.0%
 
2020-11-30T02:58:04.404573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique35 ?
Unique (%)0.6%
2020-11-30T02:58:04.616460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length86
Median length12
Mean length12.33174733
Min length2
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
11-50
1065 
201-500
833 
51-100
756 
101-200
686 
10001+
557 
Other values (5)
2198 
ValueCountFrequency (%) 
11-50106517.5%
 
201-50083313.7%
 
51-10075612.4%
 
101-20068611.3%
 
10001+5579.1%
 
501-10005519.0%
 
1-105238.6%
 
2001-50004026.6%
 
1001-20003676.0%
 
5001-100003555.8%
 
2020-11-30T02:58:04.810022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:04.967023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:05.193384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length7
Mean length6.695159967
Min length4
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
Producto basado en Software
2200 
Servicios / Consultoría de Software / Digital
2135 
Otras industrias
1760 
ValueCountFrequency (%) 
Producto basado en Software220036.1%
 
Servicios / Consultoría de Software / Digital213535.0%
 
Otras industrias176028.9%
 
2020-11-30T02:58:05.384317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-30T02:58:05.675263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:58:05.872064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length45
Median length27
Mean length30.12879409
Min length16

recomendacion_laboral
Real number (ℝ≥0)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.340114848
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size47.6 KiB
2020-11-30T02:58:06.058628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.024057067
Coefficient of variation (CV)0.2757527789
Kurtosis0.6160337606
Mean7.340114848
Median Absolute Deviation (MAD)1
Skewness-0.880844545
Sum44738
Variance4.09680701
MonotocityNot monotonic
2020-11-30T02:58:06.239413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8152525.0%
 
7117119.2%
 
1089214.6%
 
987214.3%
 
65889.6%
 
54567.5%
 
42554.2%
 
31632.7%
 
1901.5%
 
2831.4%
 
ValueCountFrequency (%) 
1901.5%
 
2831.4%
 
31632.7%
 
42554.2%
 
54567.5%
 
ValueCountFrequency (%) 
1089214.6%
 
987214.3%
 
8152525.0%
 
7117119.2%
 
65889.6%
 

politicas_diversidad
Real number (ℝ≥0)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.62100082
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size47.6 KiB
2020-11-30T02:58:06.419480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.324034039
Coefficient of variation (CV)0.304951291
Kurtosis0.5630305859
Mean7.62100082
Median Absolute Deviation (MAD)2
Skewness-1.056298722
Sum46450
Variance5.401134213
MonotocityNot monotonic
2020-11-30T02:58:06.594062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10162226.6%
 
8112718.5%
 
999716.4%
 
776312.5%
 
54928.1%
 
64817.9%
 
11833.0%
 
41823.0%
 
31472.4%
 
21011.7%
 
ValueCountFrequency (%) 
11833.0%
 
21011.7%
 
31472.4%
 
41823.0%
 
54928.1%
 
ValueCountFrequency (%) 
10162226.6%
 
999716.4%
 
8112718.5%
 
776312.5%
 
64817.9%
 

tecnologies
Categorical

HIGH CARDINALITY

Distinct5452
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size47.6 KiB
ninguna de las anteriores, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores
 
219
ninguna, ninguno, ninguno, ninguna, ninguno
 
113
windows server, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores
 
27
vmware, windows server, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores
 
20
windows server, ninguno, ninguno, ninguna, ninguno
 
12
Other values (5447)
5704 
ValueCountFrequency (%) 
ninguna de las anteriores, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores2193.6%
 
ninguna, ninguno, ninguno, ninguna, ninguno1131.9%
 
windows server, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores270.4%
 
vmware, windows server, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores200.3%
 
windows server, ninguno, ninguno, ninguna, ninguno120.2%
 
linux, ninguno de los anteriores, ninguno de los anteriores, ninguna de las anteriores, ninguno de los anteriores100.2%
 
mainframe, cobol, ninguno de los anteriores, ibm db2, ninguno de los anteriores90.1%
 
ninguna de las anteriores, javascript, react.js, ninguna de las anteriores, visual studio code90.1%
 
vmware, windows server, ninguno, ninguno, ninguna, ninguno80.1%
 
windows server, ninguno de los anteriores, ninguno de los anteriores, microsoft sql server, ninguno de los anteriores80.1%
 
Other values (5442)566092.9%
 
2020-11-30T02:58:06.837292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5296 ?
Unique (%)86.9%
2020-11-30T02:58:07.100168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length716
Median length123
Mean length134.8252666
Min length13

Interactions

2020-11-30T02:57:36.200286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:36.483256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:36.646269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:36.788509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:36.951234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.108332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.254396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.395136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.543263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.696602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:37.860799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:38.074946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:38.424148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:38.709097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:39.028050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:39.344422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:39.603896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:39.810891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.005177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.204538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.429169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.614478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.773401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:40.925059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.082451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.226370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.378794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.538519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.695838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:41.840744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.000191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.283829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.427310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.575022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.742464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:42.883656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.030362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.185350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.329194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.468335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.619396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.773136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:43.941587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:44.089787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:44.229502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:44.378838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:44.534670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:44.720910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.040672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.235587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.409776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.568567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.729733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:45.887617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.034636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.190204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.349742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.510237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.659936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.813970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:46.967912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:47.120375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:47.263660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:47.411408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-30T02:58:07.295633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-30T02:58:07.564077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-30T02:58:07.809067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-30T02:58:08.086214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-30T02:58:08.514345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-30T02:57:47.840165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:49.896031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:50.347022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-30T02:57:50.581197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

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Duplicate rows

Most frequent

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1Hombre27Entre Ríos7.02.03UniversitarioEn cursoLicenciatura en AdministraciónUSAL - Universidad del SalvadorSí, de forma particularNoNoManager / DirectorWindowsiOSNoRemoto (empresa de otro país)550000.0Mi sueldo está dolarizado4NoNo0.0JamásHeterosexual101-200Servicios / Consultoría de Software / Digital1010ninguna de las anteriores, ninguno de los anteriores, ninguno de los anteriores, microsoft azure (tables, cosmosdb, sql, etc), ninguno de los anteriores2
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